Estimating plant physiological indicators with remote sensing technology is critical for ensuring precise field management. Compared with other remote sensing platforms, low-altitude unmanned aerial vehicles (UAVs) produce images with high spatial resolution that can be used to clearly identify vegetation. However, the information of UAV image data is relatively complex and difficult to analyze, which is the main problem limiting its large-scale use at present. In order to monitor plant physiological indexes from the multi-spectral data, a new method based on machine learning is studied in this paper. Using UAV for deriving the absorption coefficients of plant canopies and whole leaf area, this paper quantifies the effects of plant physiological indicators such as the soil and plant analyzer development (SPAD) value, whole leaf area, and dry matter accumulation on the relationship between the reflectance spectra. Nine vegetation indexes were then extracted as the sensitive vegetation indexes of the rice physiological indicators. Using the SVM model to predict the SPAD value of the plant, the mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and symmetric mean absolute percentage error (SMAPE) values of the model were 1.90, 1.38, 0.13, 0.86, and 4.13, respectively. The results demonstrate that the rice plants display a considerable biochemical and spectral correlation. Using SVM to predict the SPAD value has a better effect because of a better adaptation and a higher accuracy than other models. This study suggests that the multi-spectral data acquired using UAV can quickly estimate field physiological indicators, which has potential in the pre-visual detection of SPAD value information in the field. At the same time, it can also be extended to the detection and inversion of other key variables of crops.

Peanut flavor is a complex and important trait affected by raw material and processing technology owing to its significant impact on consumer preference. In this research, principal component analysis (PCA) on 33 representative traits associated with flavor revealed that total sugars, sucrose, and total tocopherols provided more information related to peanut flavor. Genome-wide association studies (GWAS) using 102 U.S. peanut mini-core accessions were performed to study associations between 12,526 single nucleotide polymorphic (SNP) markers and the three traits. A total of 7 and 22 significant quantitative trait loci (QTLs) were identified to be significantly associated with total sugars and sucrose, respectively. Among these QTLs, four and eight candidate genes for the two traits were mined. In addition, two and five stable QTLs were identified for total sugars and sucrose in both years separately. No significant QTLs were detected for total tocopherols. The results from this research provide useful knowledge about the genetic control of peanut flavor, which will aid in clarifying the molecular mechanisms of flavor research in peanuts.

Twenty-seven genotypes of varieties and advanced breeding lines were grown in two locations in three years with three replications to estimate the effects of the genotype-by-environment interaction (G × E) on the oil and oleic fatty acid contents of cultivated peanuts. Oil and oleic fatty acid contents were quantified using NMR and GC, respectively. The tested lines were genotyped with functional SNP markers from the FAD2A and FAD2B genes using real-time PCR and classified into four genotypes. The results indicated that Alabama was the environment that better discriminated the test genotypes during the year 2012. Eight promising selected genotypes #12, #15, ARSOKR, Brantley, GaHO, M04-149, M04-48, and SunO97R showed wide adaptation and high-oleic acids of 83.02%, 81.32%, 82.03%, 81.15%, 79.21%, 80.94%, 82.46%, and 82.18%, respectively. The Additive Main Effects and Multiplicative Interaction (AMMI) model that combines the conventional analyses of variance for additive main effects with the principal component analysis (PCA) for the non-additive residuals was applied to estimate the additive effects from FAD2A and FAD2B genes and the G × E interaction. The results indicated significant G × E interactions for oleic fatty acid contents. No correlation between oil content and FAD2A and FAD2B genes was found. The FAD2B gene had a larger additive effect than the FAD2A gene. The results from this study may be useful not only for peanut breeders, but also for food processors and product consumers to select suitable cultivars.

We aimed to elucidate the possible yield-increasing mechanisms through regulation of shade-avoidance responses at both physiological and molecular levels under monoseeding. Our results revealed that monoseeding decreased the main stem height but increased the main stem diameter and the number of branches and nodes compared to the traditional double- and triple-seeding patterns. The chlorophyll contents were higher under monoseeding than that under double- and triple-seeding. Further analysis showed that this, in turn, increased the net photosynthetic rate and reallocated higher levels of assimilates to organs. Monoseeding induced the expression patterns of Phytochrome B (Phy B) gene but decreased the expression levels of Phytochrome A (Phy A) gene. Furthermore, the bHLH transcription factors (PIF 1 and PIF 4) that interact with the phytochromes were also decreased under monoseeding. The changes in the expression levels of these genes may regulate the shade-avoidance responses under monoseeding. In addition, monoseeding increased pod yield at the same population density through increasing the number of pods per plant and 100-pod weight than double- and triple-seeding patterns. Thus, we inferred that monoseeding is involved in the regulation of shade-avoidance responsive genes and reallocating assimilates at the same population density, which in turn increased the pod yield.